Rapid Landslide Mapping During the 2023 Emilia-Romagna Disaster: Assessing Automated Approaches with Limited Training Data for Emergency Response

The catastrophic rainfall events of May 2023 in Emilia-Romagna, Italy, triggered over 80,000 landslides and widespread flooding, presenting unprecedented challenges for emergency response and disaster management. This study evaluates the potential of automated landslide mapping using deep learning models, specifically U-Net and SegFormer, to address these challenges in scenarios with limited training data and time constraints. The research focuses on four severely affected municipalities—Casola Valsenio, Predappio, Modigliana, and Brisighella—leveraging a unique approach where training was conducted exclusively on one municipality (Casola Valsenio) and applied to the others.

The study assesses the performance of these models across varied geological and environmental contexts, examining the impact of input data configurations, including pre- and post-event imagery, slope, and NDVI change maps derived from high-resolution aerial and Sentinel-2 satellite data. While both models achieved notable accuracy, SegFormer demonstrated greater resilience in handling complex geological conditions. Despite challenges like false positives in agricultural fields and along river margins, the models effectively reduced the time required for initial mapping, providing a critical starting point for manual refinement.

Quantitative metrics, such as F1 score and Intersection over Union (IoU), were complemented by expert qualitative evaluations, ensuring a comprehensive assessment of the models’ practical applicability. Results reveal that automated mapping, though not a replacement for manual methods, can significantly expedite the production of high-quality landslide maps, critical for immediate disaster response. By automating the initial detection and delineation processes, these methods can save weeks of work, allowing responders to focus on refining outputs and addressing urgent needs.

This research underscores the feasibility of integrating machine learning models into emergency workflows, bridging the gap between academic advancements and practical applications. Automated mapping offers a scalable, efficient, and reliable solution for rapid disaster response, particularly in large-scale emergencies, providing a foundation for future innovations in geohazard management.